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@bearpelican
Created July 2, 2018 03:10
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Command line run:
python -m torch.distributed.launch --nproc_per_node=8 --nnodes=4 --node_rank=0 --master_addr=192.168.47.71 --master_port=6006 train_imagenet_nv.py ~/data/imagenet --save-dir ~/data/training/nv/2018-07-01T18:29:18-cluster_4_region_c_spot-0-lr1d6-e55 --loss-scale 512 --fp16 -b 192 --sz 224 -j 8 --lr 1.6 --epochs 55 --small --dist-url env:// --dist-backend nccl --distributed
~~epoch hours top1Accuracy
Distributed: init_process_group success
Loaded model
Defined loss and optimizer
Created data loaders
Begin training
~~0 0.02115771972222222 3.305
* Prec@1 0.822 Prec@5 3.305
~~1 0.03566609111111111 17.224
* Prec@1 6.060 Prec@5 17.224
~~2 0.05005269 28.455
* Prec@1 11.776 Prec@5 28.455
~~3 0.06456034111111111 34.019
* Prec@1 15.061 Prec@5 34.019
~~4 0.07905200361111112 32.674
* Prec@1 14.517 Prec@5 32.674
~~5 0.09351822416666668 54.137
* Prec@1 28.785 Prec@5 54.137
~~6 0.10799496027777777 54.297
* Prec@1 29.275 Prec@5 54.297
~~7 0.12250558722222221 53.009
* Prec@1 28.603 Prec@5 53.009
~~8 0.13693416611111112 56.458
* Prec@1 31.540 Prec@5 56.458
~~9 0.1514491991666667 61.940
* Prec@1 35.547 Prec@5 61.940
~~10 0.16591053972222225 65.229
* Prec@1 38.580 Prec@5 65.229
~~11 0.18033650444444443 61.204
* Prec@1 35.525 Prec@5 61.204
~~12 0.19471902972222224 65.893
* Prec@1 39.655 Prec@5 65.893
~~13 0.20916098222222224 63.272
* Prec@1 37.172 Prec@5 63.272
~~14 0.22353786999999997 61.278
* Prec@1 35.595 Prec@5 61.278
~~15 0.23779359194444444 64.911
* Prec@1 38.978 Prec@5 64.911
~~16 0.25222053222222224 55.502
* Prec@1 31.156 Prec@5 55.502
~~17 0.2666208247222222 62.716
* Prec@1 36.650 Prec@5 62.716
~~18 0.2809242213888889 62.480
* Prec@1 36.866 Prec@5 62.480
~~19 0.29524531388888886 68.018
* Prec@1 41.525 Prec@5 68.018
~~20 0.30958530638888887 68.152
* Prec@1 41.815 Prec@5 68.152
~~21 0.32389034777777775 63.326
* Prec@1 37.612 Prec@5 63.326
~~22 0.35748271472222226 68.718
* Prec@1 41.765 Prec@5 68.718
~~23 0.3867777436111111 75.720
* Prec@1 50.138 Prec@5 75.720
~~24 0.4160877447222222 66.377
* Prec@1 40.619 Prec@5 66.377
~~25 0.4453867519444445 66.569
* Prec@1 40.533 Prec@5 66.569
~~26 0.4747570483333333 88.396
* Prec@1 67.776 Prec@5 88.396
~~27 0.5043125308333333 88.600
* Prec@1 68.104 Prec@5 88.600
~~28 0.5336038547222222 88.958
* Prec@1 68.964 Prec@5 88.958
~~29 0.5630766452777778 89.159
* Prec@1 68.998 Prec@5 89.159
~~30 0.5923633925 89.559
* Prec@1 70.034 Prec@5 89.559
~~31 0.6216736391666667 89.421
* Prec@1 69.464 Prec@5 89.421
~~32 0.6510797169444444 89.551
* Prec@1 69.716 Prec@5 89.551
~~33 0.6803207261111112 89.115
* Prec@1 68.926 Prec@5 89.115
~~34 0.70979135 89.669
* Prec@1 69.840 Prec@5 89.669
~~35 0.7390890566666667 89.061
* Prec@1 69.040 Prec@5 89.061
~~36 0.7682257322222221 89.321
* Prec@1 69.336 Prec@5 89.321
~~37 0.7975089430555555 88.822
* Prec@1 68.508 Prec@5 88.822
~~38 0.8267278697222222 88.308
* Prec@1 67.426 Prec@5 88.308
~~39 0.8559170680555556 89.333
* Prec@1 69.470 Prec@5 89.333
~~40 0.8852173652777777 89.339
* Prec@1 69.446 Prec@5 89.339
~~41 0.9146027002777777 88.908
* Prec@1 68.612 Prec@5 88.908
~~42 0.9437983355555556 87.722
* Prec@1 66.587 Prec@5 87.722
~~43 0.9731793472222222 91.557
* Prec@1 73.299 Prec@5 91.557
~~44 1.0026220183333332 91.599
* Prec@1 73.570 Prec@5 91.599
~~45 1.0318271805555557 91.697
* Prec@1 73.752 Prec@5 91.697
~~46 1.0611022966666668 91.727
* Prec@1 73.860 Prec@5 91.727
~~47 1.0902345866666667 91.823
* Prec@1 73.958 Prec@5 91.823
~~48 1.119547578888889 91.865
* Prec@1 73.960 Prec@5 91.865
~~49 1.1486920980555555 91.823
* Prec@1 73.886 Prec@5 91.823
~~50 1.17787483 91.859
* Prec@1 74.032 Prec@5 91.859
~~51 1.2623385825 92.922
* Prec@1 75.342 Prec@5 92.922
~~52 1.3351064161111112 93.040
* Prec@1 75.600 Prec@5 93.040
~~53 1.4084640425000001 93.035
* Prec@1 75.662 Prec@5 93.035
~~54 1.4821460480555555 93.074
* Prec@1 75.629 Prec@5 93.074
~~55 1.5555668911111111 93.080
* Prec@1 75.737 Prec@5 93.080
~~56 1.6290011797222224 93.040
* Prec@1 75.631 Prec@5 93.040
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